From Parts to Patterns: Reframing Chronic Disease through Network Thinking

Why we need a new paradigm — and how modern science is catching up to ancient insight.

Introduction: From Machines to Networks

In the first essay of this series, I invited readers to reconsider the dominant metaphor of the body — not as a machine made of discrete, repairable parts, but as a living network: a dynamic web of rhythms, relationships, and regulatory flows.

This isn’t just poetic language. It’s a paradigm shift — one increasingly supported by emerging insights from physiology, systems science, and network biology.

I argued that the metaphors we use to understand the body shape the care we offer. They determine the questions we ask, the problems we can perceive, and the kinds of solutions we imagine.

For over a century, biomedicine has largely operated through a mechanistic lens: it seeks linear causes, targets dysfunction, and emphasizes the repair of broken parts. In contrast, many traditional and complementary medical systems have long focused on salutogenesis — the cultivation of vitality, balance, and resilience.

Each of these worldviews offers important truths — and each, on its own, has limitations.

In this essay, I’ll explore the philosophical foundations of both biomedicine and traditional healing systems, and argue that neither framework alone is fully equipped to address the complexity of modern chronic illness. But rather than discard either one, I propose a third path:

What we need now is not a rejection of science, nor a romantic return to premodern cosmologies — but a reframing: one that allows modern medicine to evolve by integrating the pattern-based, relational logic that traditional systems have long understood.

A note on language: I use the term biomedicine to describe what is often called “Western medicine.” I do so not to dismiss other ways of knowing, but to highlight a particular scientific model of the body — one grounded in reductionism, mechanism, and materialism — that has become the global standard in healthcare.

Finally, while human beings are layered — physical, emotional, ecological, and spiritual — this series begins at the physiological level, where network science already offers powerful insights. Other layers will follow.

The Worldview Beneath the White Coat

Modern biomedicine is more than a set of clinical practices or diagnostic tools — it is grounded in a particular way of knowing and reflects a specific worldview.

When people describe something as "scientific," they often invoke not just the scientific method — observation, hypothesis testing, and empirical validation — but also a deeper epistemological stance: an implicit belief about how we come to know what is true. This epistemology emerged during the Scientific Revolution of early modern Europe. Thinkers like Galileo, Descartes, Newton, and Leibniz ushered in a new paradigm that privileged empirical observation, experimentation, and mathematical description. In this framework, truth was no longer revealed by tradition or authority but discovered through systematic inquiry.

But there’s another layer embedded in the term “scientific” — not just how we know, but what we believe exists to be known. This is the realm of ontology: our assumptions about what is real and what counts. When someone dismisses an idea or practice as “unscientific,” they may not realize that their judgment reflects more than a concern for method. It reflects an allegiance — often unconscious — to a worldview that defines reality in specific, historically contingent ways.

One of the defining moves in early modern science was the separation of reality into objective and subjective domains. Descartes (1641) famously distinguished between res extensa (the extended, physical world) and res cogitans (the world of thought and consciousness).¹ Galileo and Locke extended this by separating primary qualities (quantifiable properties like mass, shape, and motion) from secondary qualities (subjective experiences like color, taste, and emotion).² Only the former were deemed truly real. The latter were relegated to the inner world of the observer — private, unreliable, and often irrelevant to science.

This redefinition of reality had profound consequences. The sensory richness of human experience — color, fragrance, sensation — was reduced to electrochemical signals and mathematical functions. Newton translated the rainbow into wavelengths; scent became the shape of molecules binding to receptors. A worldview that once included soul, spirit, and vital force gradually narrowed to one of material mechanisms and measurable phenomena.

This wasn’t merely a shift in method — it was a transformation in metaphysics. It gave rise to what philosophers call metaphysical naturalism: the foundational ontology underlying modern science and medicine.³ This worldview rests on three key assumptions:⁴

• Physicalism (Materialism): In this view, only physical substances—atoms, molecules, genes, neural circuits—and the known physical forces (such as gravity, electromagnetism, and nuclear interactions) are considered fundamentally real. What cannot be measured, quantified, or experimentally verified is often dismissed as subjective, secondary, or illusory. Concepts like a “vital force” are excluded by definition. Physicochemical mechanisms are assumed to fully explain metabolism, embryological development, and other life processes. Mind is reduced to brain, treated as an epiphenomenon—a byproduct of neuronal activity—while consciousness is framed as an emergent feature of neural computation, not as a distinct or primary phenomenon in its own right.

• Reductionism: To understand a system, one must break it into its smallest parts. Health is explained through molecules; dysfunction through biochemical errors. The body is approached not as a dynamic, integrated whole, but as an assemblage of discrete units—tissues, organs, enzymes—to be analyzed, targeted, and repaired individually.

• Mechanism: In this paradigm, biological systems are conceptualized as machines—composed of parts that interact through predictable, linear cause-and-effect. Causality is deterministic: inputs produce outputs in stepwise fashion, echoing Newton’s clockwork universe. Complex phenomena are interpreted as molecular chains of events: a hormone binds to a receptor, a cascade is triggered, a response ensues. Within this framework, notions such as “vital force,” “energy,” or “organizing intelligence” are dismissed as non-scientific artifacts, incompatible with biomedical discourse. This is a “billiard-ball” logic—where each effect is the result of a discrete interaction, with little space for emergence, context, or systemic intelligence.

It’s important to note: many who critique biomedicine are not rejecting science per se. They are rejecting this ontological reduction — the view that all of life can be explained through material parts and mechanical processes. Such critics recognize that human experience, health, and illness unfold in complex, interdependent systems — not all of which lend themselves to linear analysis or quantification.

To be fair, the biomedical model has yielded tremendous progress — especially in acute/emergency care, trauma, and surgical intervention. When a bone breaks, an artery is blocked, or a pathogen must be eradicated, the precision of this model is unmatched.

These philosophical foundations of biomedicine did not remain abstract. They materialized in real-world institutions: in the architecture of the hospital, the logic of the laboratory, and the language of genomics. Medical education, regulatory systems, and diagnostic technologies all reflect — and reinforce — this mechanistic worldview. Over time, the body came to be seen not as a living ecosystem in dynamic relationship with its environment, but as a machine, and medicine as the science of parts.

The Rise of the Machine Model

To understand why modern medicine often feels so mechanical — focused on fixing broken parts — we need to look back at how biomedicine was born. What we now call biomedicine is actually a relatively recent invention. Though it draws on centuries-old anatomical and chemical knowledge, its distinct form emerged over the last two hundred years, shaped by groundbreaking scientific discoveries that redefined both the human body and disease.

In 1827, chemist Friedrich Wöhler challenged the old belief that life required a mysterious “vital force” when he synthesized urea from inorganic compounds.⁵ This breakthrough gave birth to biochemistry and the idea that life’s processes could be explained by chemistry and physics alone — no special life force needed.

In the 1850s, Rudolf Virchow revolutionized medicine by reframing disease not as an imbalance of humors or energies, but as dysfunction at the cellular level (cellular theory of disease).⁶ From then on, understanding cells became the foundation of pathology.

Between the 1860s and 1880s, the germ theory of disease took hold, locating illness in invading microbes — further objectifying disease and positioning the body as a battlefield against external enemies.⁷

A key milestone in the development of modern biomedicine was the 1910 Flexner Report, commissioned by the American Medical Association to evaluate and reform medical education.⁸ Flexner’s recommendations emphasized a rigorous, science-based, mechanical approach centered on laboratory science and clinical training linked to hospitals. While this reform raised educational standards, it also led to the closure of many medical schools that did not or could not adopt this model, effectively institutionalizing a curriculum focused on anatomy, laboratory science, and mechanistic thinking.

Together, these and similar advances set the stage for 20th-century breakthroughs in enzymology, molecular biology, and genetics — all painting life as a series of biochemical reactions governed by physical laws.

The Human Genome Project, completed in the early 2000s, was celebrated as the key to unlocking the “blueprint of life.”⁹ The genome was seen as the master code explaining everything from cell function to human behavior.

From this emerged genetic determinism — the belief that genes are the central drivers of biology and hold the key to disease and cure. If genes are the source code, then diseases could be “debugged” by targeting faulty genes, receptors, or enzymes.

This idea underpins today’s precision medicine: therapies tailored to an individual’s unique genetic and biological profile, designed to intervene with laser-like focus rather than one-size-fits-all treatment.¹⁰

Examples of precision therapies include monoclonal antibodies that block specific cytokine receptors, mRNA vaccines that instruct the body to produce targeted antigens, and GLP-1 receptor agonists—such as Ozempic and Wegovy—that precisely regulate metabolic pathways.

Paired with big data and machine learning, precision medicine promises cures for many chronic diseases — from cardiometabolic disorders to Alzheimer’s and cancer.

These targeted therapies can be remarkably effective in the short term or for symptom relief, especially for acute or well-defined molecular conditions. But they also reflect a deeper logic: health is modeled as the absence of molecular errors, disease as a malfunctioning component, and treatment as a targeted fix.

Where the Model Breaks Down

While powerful, the precision medicine and the machine model reveals its limitations when faced with complex, chronic illnesses—such as persistent fatigue, fibromyalgia, IBS, long COVID, autoimmune overlap, and multisystem inflammatory states—that cannot be traced to a single malfunctioning molecule or pathway.

These are not disorders of isolated malfunction. They are signs of network disruption — breakdowns in physiological regulation that manifest across multiple systems. Subtle. Dynamic. Nonlinear. Often, nothing appears “broken” on standard tests, because what’s impaired is integration, not anatomy.

And yet, medicine continues to search for the one target — the rogue molecule, the overactive pathway, the missing receptor — as though complex disorders could be solved with molecular marksmanship.

Find the target. Suppress it. Replace it. Block it. Fix it.

Even precision medicine, with all its genomic sophistication, largely follows this same logic. It relentlessly hunts for ever narrower targets, operating on the belief that greater specificity will translate into better outcomes. Yet in this pursuit, it often loses sight of systemic coherence, leading to increasingly costly interventions that address isolated parts of the body without restoring the health of the whole.

As 19th-century physician Samuel Hahnemann observed, this is the essence of allopathy: treating symptoms by opposing them.¹¹ Anti-inflammatories for inflammation. Antidepressants for sadness. Immune suppressants for immune activation. Precision medicine doesn’t depart from this logic — it sharpens it.

Yes, we now have more precise tools. But in many cases, we are still treating targets, when the real issue lies in patterns — patterns of breakdown, imbalance, and disconnection within the body’s complex regulatory web.

When Patients and Providers Look Beyond Biomedicine

Frustrated by the limitations of conventional medicine—especially in addressing chronic, complex, or “medically unexplained” conditions—many patients and practitioners begin to question not just the tools of biomedicine, but its underlying assumptions.

This has led to growing interest in modalities often grouped under the umbrella of complementary and alternative medicine (CAM)—now more commonly referred to as integrative medicine, since they are typically used alongside conventional biomedical treatments..¹² These approaches span a wide spectrum, which can be broadly categorized into two categories:

• T-CAM (Traditional CAM): Long-standing systems of medicine rooted in centuries or millennia of clinical observation and theory, such as Traditional East Asian Medicine (TEAM), Ayurveda, Tibetan medicine, Greco-Arabic-Unani medicine, and Indigenous healing practices.

• M-CAM (Modern CAM): More recent integrative approaches, including naturopathic medicine, functional medicine, chiropractic, and osteopathy. Though often informed by traditional ideas, these systems tend to adopt biomedical language and diagnostic logic—sometimes unconsciously replicating the same mechanistic worldview they aim to challenge.

There are also hybrid or energy-based approaches (e.g., Reiki, subtle body therapies) that, while significant, warrant a separate discussion.

At their best, traditional systems offer what biomedicine often lacks: a coherent framework for interpreting complex, whole-person phenomena—not merely as symptoms to be managed, but as patterns of imbalance to be addressed. These traditions recognize that illness can arise from disruptions in rhythm, stagnation of flow, or excess and deficiency—not only from identifiable pathogens or broken parts.

The Intelligence of Life: Medicine Before Mechanism

These older systems of medicine are not simply therapeutic alternatives. They represent an entirely different way of knowing—a different epistemology and ontology.

Unlike modern biomedicine, which privileges objectivity and measurable data, traditional medical systems emerged from a phenomenological worldview, in which lived experience and meditative “technologies” were legitimate and essential sources of knowledge. In this premodern paradigm, subjective states were not seen as obstacles to objectivity but as windows into deeper patterns of health and disease. The inner world was real, and the goal of medicine was to restore harmony, not just to manage biomarkers and mechanisms.

But more fundamentally, these systems rested on a different ontology. Instead of physicalism or materialism, many traditional frameworks viewed consciousness, pattern, or information as the primary substrate of reality. The world was understood as relational and holistic, not mechanical and fragmented. The human body was not a machine but a living ecosystem—a dynamic, integrated whole whose emergent properties could not be reduced to parts.

Vitalism—often misunderstood and dismissed in modern discourse—was the organizing principle behind these medical philosophies.¹³ It held that life is animated by a non-material, directive force, one that governs growth, development, and healing.

In Hippocratic and Galenic traditions, this was expressed as the vis medicatrix naturae—the healing power of nature. Far from metaphorical, this was the core of clinical reasoning. The body’s internal terrain (milieu intérieur) was shaped by the interplay of vital substances or humors. Health (or eukrasia) was a state of dynamic balance; disease (dyskrasia) reflected disorganization, not damage. Treatment aimed to restore coherence, not suppress symptoms.¹⁴

This principle of health as a dynamic, balanced flow is a common thread across many traditional healing systems. In Classical and Traditional Chinese Medicine, it is understood as the harmonious movement of qi throughout the body. Ayurveda views health as arising from the interplay of prana, ojas, and the doshas, while Tibetan medicine emphasizes the flow of subtle winds, or rlung, moving through energetic channels. Despite differences in terminology and cultural context, each tradition highlights the importance of systemic balance and integration for well-being.

These were not conceptualized as mechanical energies, but as subtle, organizing, and informational forces—similar to what we might now describe as negentropic principles: forces that uphold structure-form and resist entropy.

The Fall (and Persistence) of Vitalism

As modern science progressed, physicalism, reductionism, and mechanistic thinking gradually supplanted earlier holistic worldviews that emphasized balance, rhythm, and the subtle interplay of forces.

By the mid-19th century, a decisive shift had taken place. Scientific breakthroughs — already detailed earlier — had introduced powerful new ways of understanding the body. Biochemical life no longer required a “vital force” to explain it. Disease was reconceived as a malfunction of cells rather than an imbalance of humors or energies. And infection came to be seen as a battle between microbe and host, not a disruption of the body's internal terrain.

These discoveries were not wrong — they were transformative. But they came at a cost: the symbolic and relational logic of earlier medical systems was discarded or misinterpreted through a materialist lens.

Nowhere is this clearer than in the fate of the humoral model. Once understood as a metaphorical and energetic map — representing tendencies, constitutions, and patterns of imbalance — the humors were reinterpreted as literal fluids. This mechanization led to aggressive, often harmful interventions: bloodletting, emetics, mercury purgatives. The result? A discrediting of the model, not because it lacked insight, but because its original logic had been forgotten.

Although vitalism was discredited in scientific circles, its core intuition — that life is governed by an inherent intelligence and tendency toward balance — never fully disappeared. Instead, it was translated into new languages: first physiological, then cybernetic, and now informational. The language of “forces” gave way to that of feedback, regulation, and adaptive complexity. In this way, vitalism didn't just fall — it evolved.

From Vital Force to Feedback Loops

Although vitalism was formally rejected by mainstream science, its core intuition—that life expresses an inherent intelligence capable of self-regulation, adaptation, and healing—was never fully discarded. Instead, it was reframed, first through physiology, then in systems theory and network physiology.

In the 1850s, Claude Bernard recast the vis medicatrix naturae as the milieu intérieur—the stable internal environment essential for life.¹⁵ Building on this, in 1926 Walter Cannon introduced the concept of homeostasis, describing how the body maintains equilibrium through feedback loops.¹⁶

Starting in the 1930s, Hans Selye mapped the physiology of stress adaptation, showing how the HPA and SAM axes help the body maintain balance under pressure.¹⁷ These foundational ideas paved the way for cybernetics (Norbert Wiener, 1948)—the science of regulation and communication in biological and mechanical systems.¹⁸

The story continued with psychoneuroimmunology (Ader and Cohen, 1975), and later psychoneuroendocrine-immunology, which revealed the deep integration of the psyche with the nervous, endocrine, and immune systems through complex regulatory networks.¹⁹

By the 1990s, the idea of homeodynamics (Yates, 1994) took hold—the understanding that health is not about holding a fixed point, but about maintaining rhythmic adaptability within dynamic ranges.²⁰ This concept echoes ancient medical traditions that viewed health as flow rather than stasis.

What was once called vital force is now understood as a self-organizing system, a regulatory network, or an emergent pattern of dynamic equilibrium.

This perspective was more than theoretical. Early in the 20th century, embryologist Hans Driesch observed that the development of a whole organism from a single cell defied purely mechanistic explanations. He proposed the existence of an entelechy—a non-material organizing force guiding form and function.²¹ Though often seen as the last formal vitalist in Western science, Driesch’s insights lived on.

In the 1930s, thinkers such as Paul Weiss, Ross Harrison, Joseph Needham, and Ludwig von Bertalanffy helped usher in organicism in biology—an evolution of vitalist thought that emphasized the emergent properties of living systems arising from the relationships between parts, not merely the parts themselves.²²

This shift opened the door to modern fields like systems biology, complexity theory, and network physiology—disciplines that emphasize interconnection, feedback, and nonlinear dynamics as the true engines of life.

In this light, modern systems science and network physiology can be seen as vitalism reimagined—not abandoned, but translated into new metaphors, methods, and models of health and healing.

A Blind Spot in Contemporary Natural Medicine

Ironically, while many modern CAM approaches invoke the vis medicatrix naturae and vitalism as foundational principles—and aim to move beyond the reductionism of conventional medicine—they often end up replicating the same mechanistic logic, simply substituting different tools. Clinging to terms like “vitalism” and “vis medicatrix naturae” without embracing newer, more robust conceptual frameworks can feel backward and limiting in today’s evolving medical landscape.

Instead of pharmaceuticals, CAM targets disease with herbs, supplements, detoxes, or restrictive diets. Instead of suppressing symptoms, it seeks a root cause: food sensitivities, mold, mitochondrial dysfunction, leaky gut. Yet the underlying framework often remains linear and target-based: identify a single cause, find the faulty part, fix it.

Even herbal medicine—deeply rooted in ecological, energetic, and relational traditions—has been reshaped by biomedical logic. Extracts are standardized to isolated active constituents, prescribed in high doses, and aimed at specific molecular targets. This approach echoes pharmacology more than ecology.

This is not to dismiss these methods—many patients have found meaningful relief and healing through them. But it highlights a deeper challenge: changing the substances we use does not necessarily change the worldview we operate from.

If we truly want to transcend the limitations of Biomedicine 1.0, we must do more than swap inputs. We need a new framework—one that sees health not merely as the absence of dysfunction but as the presence of pattern, coherence, adaptability, and dynamic balance.

This is the promise of network physiology—and the beginning of what we might call Biomedicine 2.0.

Toward Biomedicine 2.0

To address the growing burden of complex, chronic, and systemic illness, the foundational assumptions of Biomedicine 1.0—particularly its ontology of reductionism and linear mechanism—must evolve.

But the path forward is not to abandon science, nor to regress into premodern cosmologies wholesale. The scientific method remains one of humanity’s most powerful epistemological tools. What must change is not science itself, but the conceptual framework through which we interpret complex phenomena like health, illness, and healing.

While traditional and complementary medicine (T-CAM) systems—Ayurveda, Chinese medicine, Unani, Tibetan medicine—continue to be practiced and have much to offer, their classical languages and cosmologies often sit uneasily alongside modern biomedical discourse. Many view them as metaphorical, poetic, or prescientific—insightful perhaps, but lacking the conceptual rigor to stand beside modern pharmacology, molecular biology, or genomics.

Calls to “subject these traditions to science” are common. But as we've seen, Biomedicine 1.0—rooted in linear causality and molecular targeting—lacks the tools to recognize or model the kind of dynamic, emergent, and relational phenomena that traditional systems describe as patterns. It’s not that traditional medicine resists science—it’s that the science we’ve inherited has not yet caught up to the complexity these traditions have long observed.

What we need is a Biomedicine 2.0—a new paradigm that preserves the epistemological strengths of modern science, while embracing a more nuanced ontology: one that recognizes systems, networks, and patterns of interaction as primary features of biological reality.

This is where network physiology enters the scene.

Rather than focusing solely on isolated targets—genes, receptors, or biomarkers—Biomedicine 2.0 must shift toward recognizing, measuring, and influencing patterns: multi-system dysregulations, relational breakdowns, disrupted rhythms, and incoherent signaling networks. In this expanded framework, information—the connections and interactions that link the parts of a system—becomes as crucial as the physical components themselves, transforming both diagnosis and treatment.

Importantly, this shift does not leave behind herbal medicine. On the contrary, the genius of herbal traditions may lie precisely in their ability to treat patterns rather than parts. The synergistic actions of complex plant compounds, especially when combined in thoughtfully designed formulas, are not reducible to single “active constituents.” They interact with networks, not just nodes.

Biomedicine 2.0 invites us to move from reductionism to holism—not as a vague ideal, but as a scientifically grounded shift from isolated parts to integrated networks. From mechanism to organicism—a model in which nonlinear, dynamic relationships govern the behavior of the system as a whole.

In my next essay, I’ll explore how the language of pattern found in traditional medical systems can be translated into the language of systems biology, signaling networks, and regulatory coherence—not to erase their origins, but to integrate them into a medicine that honors both tradition and science.

Note: Images in this post were generated with the assistance of an AI model.

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